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Publication Type : Journal Article
Publisher : Journal of Intelligent and Fuzzy Systems, IOS Press
Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 36, Number 3, p.2067-2073 (2019)
Keywords : Classification accuracy, Convolution, Convolutional neural network, De-noising, GURLS, Hyperspectral imaging, IBBC, image classification, Image denoising, Image processing applications, Intelligent systems, LIBSVM, Neural networks, Reflection, Regularized Least Squares, Soft computing, Spectroscopy, Support vector machines
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Hyperspectral Image (HSI) store the reflectance values of a single scene or object in several continuous bands of electromagnetic spectrum. When the image is recorded, the information in some of the spectral bands gets mixed with noise. The classification accuracy of hyperspectral image varies inversely with the quantity and nature of noise present in the cluster of spectral bands. Thus, denoising is a fundamental prerequisite in image processing applications like classification, unmixing, etc. In this paper, we compare the effect of denoising via classification using Vectorized Convolutional Neural Network (VCNN), kernel based Support Vector Machine (SVM) and Grand Unified Regularized Least Squares (GURLS) classifiers. The classifiers are provided with raw data (without denoising) and denoised data using spectral and spatial Least Square (LS) techniques. The data given to the network are in the form of pixels, so we call the convolutional neural network (CNN) as VCNN. The experiments are performed on three standard HSI datasets. The performance of the classifiers are evaluated based on overall and class-wise accuracy. © 2019 - IOS Press and the authors.
Cite this Research Publication : N. V. Jacob, Sowmya, and Dr. Soman K. P., “Effect of denoising on hyperspectral image classification using deep networks and kernel methods”, in Journal of Intelligent and Fuzzy Systems, 2019, vol. 36, pp. 2067-2073.